Existing techniques for wind farm generation forecasting are often based on wind speed forecast, which is subsequently translated into the wind power output, and generally assume that wind generation from the farm can be directly calculated as a function of wind speed recorded at one specific location in the farm. In reality, however, the relationship between wind speed observed at a location in the farm and the aggregate wind generation from the farm is far more complicated than a simple transformation based on the turbine power curve. In fact, the power outputs from identical turbines within a farm are not necessarily equal, even if the turbines are co-located, and this disparity or “mismatch” is particularly severe when they are far apart. Therefore, the applicability of the prior efforts is rather limited, particularly when the farm has a large number of turbines distributed over an extended geographical area. Another method for wind power forecast used in the power industry is based on persistent forecast, which assumes the wind generation remains the same in the epoch. However, such an approach would not work well in the event of wind ramps. Thus, there is an urgent need to develop a more systematic approach to both distributional forecast and point forecast of wind farm power generation. Principles of the present disclosure meet this need, taking a statistical-grounded approach based on historical data.